In [1]:
from pyforest import *
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import plotly.subplots as sp
from plotly.subplots import make_subplots
import sidetable
import math
import folium
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import geopandas as gpd
import emoji
import warnings
warnings.filterwarnings('ignore')
In [2]:
fifa = pd.read_csv("https://raw.githubusercontent.com/EylonYehiel/projects/main/Data/Fifa%2023%20Players%20Data.csv")
In [3]:
fifa.sample(5)
Out[3]:
| Known As | Full Name | Overall | Potential | Value(in Euro) | Positions Played | Best Position | Nationality | Image Link | Age | ... | LM Rating | CM Rating | RM Rating | LWB Rating | CDM Rating | RWB Rating | LB Rating | CB Rating | RB Rating | GK Rating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15152 | Park Yong Ji | Yong Ji Park | 60 | 60 | 250000 | ST,RW | ST | Korea Republic | https://cdn.sofifa.net/players/212/450/23_60.png | 29 | ... | 60 | 53 | 60 | 43 | 40 | 43 | 42 | 36 | 42 | 18 |
| 5523 | S. Bossut | Sammy Bossut | 69 | 69 | 150000 | GK | GK | Belgium | https://cdn.sofifa.net/players/142/258/23_60.png | 36 | ... | 32 | 35 | 32 | 29 | 33 | 29 | 28 | 29 | 28 | 69 |
| 14348 | R. Schifferl | Raphael Schifferl | 61 | 68 | 625000 | CB | CB | Austria | https://cdn.sofifa.net/players/270/116/23_60.png | 22 | ... | 48 | 53 | 48 | 57 | 61 | 57 | 59 | 63 | 59 | 18 |
| 2971 | C. Akpom | Chuba Akpom | 72 | 75 | 3100000 | ST | ST | England | https://cdn.sofifa.net/players/213/418/23_60.png | 26 | ... | 69 | 62 | 69 | 53 | 50 | 53 | 51 | 48 | 51 | 19 |
| 13039 | K. Kaissis | Kai-Sotirios Kaissis | 63 | 64 | 575000 | CAM,CM | CAM | Germany | https://cdn.sofifa.net/players/270/026/23_60.png | 25 | ... | 64 | 59 | 64 | 53 | 51 | 53 | 52 | 47 | 52 | 18 |
5 rows × 89 columns
In [4]:
fifa.shape
Out[4]:
(18539, 89)
In [5]:
fifa.stb.missing().sum()
Out[5]:
missing 0.0 total 1649971.0 percent 0.0 dtype: float64
In [6]:
fifa.columns
Out[6]:
Index(['Known As', 'Full Name', 'Overall', 'Potential', 'Value(in Euro)',
'Positions Played', 'Best Position', 'Nationality', 'Image Link', 'Age',
'Height(in cm)', 'Weight(in kg)', 'TotalStats', 'BaseStats',
'Club Name', 'Wage(in Euro)', 'Release Clause', 'Club Position',
'Contract Until', 'Club Jersey Number', 'Joined On', 'On Loan',
'Preferred Foot', 'Weak Foot Rating', 'Skill Moves',
'International Reputation', 'National Team Name',
'National Team Image Link', 'National Team Position',
'National Team Jersey Number', 'Attacking Work Rate',
'Defensive Work Rate', 'Pace Total', 'Shooting Total', 'Passing Total',
'Dribbling Total', 'Defending Total', 'Physicality Total', 'Crossing',
'Finishing', 'Heading Accuracy', 'Short Passing', 'Volleys',
'Dribbling', 'Curve', 'Freekick Accuracy', 'LongPassing', 'BallControl',
'Acceleration', 'Sprint Speed', 'Agility', 'Reactions', 'Balance',
'Shot Power', 'Jumping', 'Stamina', 'Strength', 'Long Shots',
'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties',
'Composure', 'Marking', 'Standing Tackle', 'Sliding Tackle',
'Goalkeeper Diving', 'Goalkeeper Handling', ' GoalkeeperKicking',
'Goalkeeper Positioning', 'Goalkeeper Reflexes', 'ST Rating',
'LW Rating', 'LF Rating', 'CF Rating', 'RF Rating', 'RW Rating',
'CAM Rating', 'LM Rating', 'CM Rating', 'RM Rating', 'LWB Rating',
'CDM Rating', 'RWB Rating', 'LB Rating', 'CB Rating', 'RB Rating',
'GK Rating'],
dtype='object')
In [7]:
fifa.describe()
Out[7]:
| Overall | Potential | Value(in Euro) | Age | Height(in cm) | Weight(in kg) | TotalStats | BaseStats | Wage(in Euro) | Release Clause | ... | LM Rating | CM Rating | RM Rating | LWB Rating | CDM Rating | RWB Rating | LB Rating | CB Rating | RB Rating | GK Rating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18539.000000 | 18539.000000 | 1.853900e+04 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 1.853900e+04 | ... | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 | 18539.000000 |
| mean | 65.852042 | 71.016668 | 2.875461e+06 | 25.240412 | 181.550839 | 75.173904 | 1602.114569 | 357.946221 | 8824.537462 | 5.081688e+06 | ... | 58.451319 | 57.374076 | 58.451319 | 56.281569 | 55.928583 | 56.281569 | 55.650251 | 54.528184 | 55.650251 | 23.257134 |
| std | 6.788353 | 6.192866 | 7.635129e+06 | 4.718163 | 6.858097 | 7.013593 | 273.160237 | 39.628259 | 19460.531154 | 1.467203e+07 | ... | 13.987122 | 13.171194 | 13.987122 | 13.903836 | 13.872190 | 13.903836 | 14.159466 | 14.743929 | 14.159466 | 15.108925 |
| min | 47.000000 | 48.000000 | 0.000000e+00 | 16.000000 | 155.000000 | 49.000000 | 759.000000 | 224.000000 | 0.000000 | 0.000000e+00 | ... | 18.000000 | 18.000000 | 18.000000 | 17.000000 | 19.000000 | 17.000000 | 17.000000 | 18.000000 | 17.000000 | 10.000000 |
| 25% | 62.000000 | 67.000000 | 4.750000e+05 | 21.000000 | 177.000000 | 70.000000 | 1470.000000 | 331.000000 | 1000.000000 | 6.650000e+05 | ... | 54.000000 | 53.000000 | 54.000000 | 51.000000 | 48.000000 | 51.000000 | 49.000000 | 45.000000 | 49.000000 | 17.000000 |
| 50% | 66.000000 | 71.000000 | 1.000000e+06 | 25.000000 | 182.000000 | 75.000000 | 1640.000000 | 358.000000 | 3000.000000 | 1.500000e+06 | ... | 62.000000 | 60.000000 | 62.000000 | 59.000000 | 59.000000 | 59.000000 | 59.000000 | 58.000000 | 59.000000 | 18.000000 |
| 75% | 70.000000 | 75.000000 | 2.000000e+06 | 29.000000 | 186.000000 | 80.000000 | 1786.000000 | 385.000000 | 8000.000000 | 3.400000e+06 | ... | 67.000000 | 66.000000 | 67.000000 | 66.000000 | 66.000000 | 66.000000 | 65.000000 | 66.000000 | 65.000000 | 20.000000 |
| max | 91.000000 | 95.000000 | 1.905000e+08 | 44.000000 | 206.000000 | 105.000000 | 2312.000000 | 502.000000 | 450000.000000 | 3.667000e+08 | ... | 92.000000 | 91.000000 | 92.000000 | 88.000000 | 89.000000 | 88.000000 | 87.000000 | 90.000000 | 87.000000 | 90.000000 |
8 rows × 71 columns
In [8]:
def bold(text):
return ("\033[1m" + f"{text}" + "\033[0m")
Basic-Features Distributions¶
In [9]:
fig = go.Figure()
fig.add_trace(go.Histogram(x=fifa['Age'], name='Age', xbins = dict(size=1)))
fig.add_trace(go.Histogram(x=fifa['Height(in cm)'], name='Height (cm)'))
fig.add_trace(go.Histogram(x=fifa['Weight(in kg)'], name='Weight (kg)'))
fig.update_layout(height=600, width=1100, title='Histograms of Attributes :', xaxis_title_text='Attribute Value', yaxis_title_text='Frequency')
In [10]:
df = pd.DataFrame(fifa.head(1000))
numeric_columns = ['Overall', 'Value(in Euro)', 'Wage(in Euro)', 'Age']
sns.set(style="ticks")
plt.figure(figsize=(5,5))
sns.pairplot(df[numeric_columns])
plt.show();
<Figure size 360x360 with 0 Axes>
In [11]:
foot = fifa['Preferred Foot'].value_counts()
trace1 = go.Pie(labels=['Right','Left'],values=[foot[0],foot[1]],
hoverinfo='label+value', textinfo='percent', textfont=dict(size=18),
marker=dict(colors=['#DC143C','#FFA500'],line=dict(color='#FFFFFF', width=4)), pull=[0, 0.1])
left_percentage = 11.5 # Average percantage of left-handed people (Wikipedia)
right_percentage = 88.5
trace2 = go.Pie(labels=['Right', 'Left'], values=[right_percentage, left_percentage],
hoverinfo='label+percent', textinfo='percent', textfont=dict(size=18),
marker=dict(colors=['#DC143C', '#FFA500'], line=dict(color='#FFFFFF', width=4)), pull=[0, 0.1])
In [12]:
fig = make_subplots(1, 2, specs=[[{'type':'domain'}, {'type':'domain'}]], subplot_titles=['Population', 'Footballers'])
fig.add_trace(trace1, 1, 2)
fig.add_trace(trace2, 1, 1)
fig.update_layout(title_text='Strong side (Right or Left) - football players VS whole population :', height = 600, width = 1000)
fig.show()
- Assuming being left-handed means left with legs either. #### It can be seen that the percentage of left-strong-sided among football players is more than twice the percentage of left-handed people in the general population. Coincidence?
By Positions :¶
In [13]:
fig = px.histogram(fifa, x="Age", color="Best Position", nbins=55)
fig.update_layout(height=450, width=1100, title='Ages distribution by positions', yaxis_title_text='Frequency')
fig.show()
fig = px.histogram(fifa, x="Weight(in kg)", color="Best Position", nbins=55)
fig.update_layout(height=450, width=1100, title='Weight distribution by positions', yaxis_title_text='Frequency')
fig.show()
fig = px.histogram(fifa, x="Height(in cm)", color="Best Position", nbins=50)
fig.update_layout(height=450, width=1100, title='Height distribution by positions', yaxis_title_text='Frequency')
fig.show()
In [14]:
fig, ax = plt.subplots(figsize=(20,15))
sns.heatmap(fifa.corr(), cmap='coolwarm', vmin=-1, vmax=1, fmt='.2f', linewidths=0.05, ax=ax)
ax.set_title('Correlation Matrix of All Attributes :\n', fontsize=25)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(labelsize=15)
plt.show()
Not really readable, we need to reduce the load¶
In [15]:
fig = px.imshow(fifa[['Overall','Reactions','Composure','Passing Total','Wage(in Euro)','Value(in Euro)','Shot Power','Age']].corr(), color_continuous_scale='OrRd', zmin=0, zmax=1)
fig.update_layout(width=800, height=600, title='Strong Connnections Zoom-in:')
fig.show()
Random forest model to predict player's overall rank by his reactions, composure and passing scores, his wage and his age :¶
In [16]:
features = ['Reactions', 'Composure','Passing Total', 'Wage(in Euro)', 'Age']
target = 'Overall'
X_train, X_test, y_train, y_test = train_test_split(fifa[features], fifa[target], test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
Test:¶
In [17]:
mse = mean_squared_error(y_test, y_pred)
rmse = mse ** 0.5
r2_score = model.score(X_test, y_test)
print("RMSE: " + bold(rmse))
print("r^2: " + bold(r2_score))
RMSE: 2.397600706049064 r^2: 0.8728423345934998
In [18]:
importances = model.feature_importances_
print("Feature Importances:")
for feature, importance in zip(features, importances):
print(f"{feature}: {importance}")
Feature Importances: Reactions: 0.7533940990013311 Composure: 0.047712616582223796 Passing Total: 0.07441100885822209 Wage(in Euro): 0.08892444372035126 Age: 0.03555783183787177
In [19]:
fig = px.scatter(x=y_test, y=y_pred, labels={'x': 'Actual Score', 'y': 'Predicted Score'}, title='Actual vs. Predicted Overall Ratings:',
trendline='ols')
fig.update_layout(height = 500)
fig.show()
Create prediction function:¶
In [20]:
def pred(Reactions, Composure, Pass, Wage, Age):
p = model.predict([[Reactions,Composure,Pass,Wage,Age]])
print(f"Overall score for {Reactions, Composure, Pass, Wage, Age} values is : {float(p)}")
In [21]:
pred(81,64,90,50000,28)
pred(78,86,70,100000,30)
pred(94,90,60,700000,21)
pred(94,80,80,60000,22)
Overall score for (81, 64, 90, 50000, 28) values is : 83.17 Overall score for (78, 86, 70, 100000, 30) values is : 79.7 Overall score for (94, 90, 60, 700000, 21) values is : 90.52 Overall score for (94, 80, 80, 60000, 22) values is : 85.2
In [22]:
general_features = ['Full Name', 'Age', 'Height(in cm)', 'Weight(in kg)', 'Nationality','Overall','Potential',
'Value(in Euro)', 'Wage(in Euro)', 'Sprint Speed', 'Defending Total', 'Dribbling Total',
'Pace Total', 'Shooting Total', 'Passing Total', 'Physicality Total', 'Best Position',
'Club Name','Preferred Foot','BaseStats','TotalStats','International Reputation','Weak Foot Rating']
Top field players :¶
In [23]:
fields = fifa[general_features][fifa['Best Position'] != 'GK']
fields.sort_values(['Overall','TotalStats','Value(in Euro)'], ascending = False).head(10)
Out[23]:
| Full Name | Age | Height(in cm) | Weight(in kg) | Nationality | Overall | Potential | Value(in Euro) | Wage(in Euro) | Sprint Speed | ... | Shooting Total | Passing Total | Physicality Total | Best Position | Club Name | Preferred Foot | BaseStats | TotalStats | International Reputation | Weak Foot Rating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | Kevin De Bruyne | 31 | 181 | 70 | Belgium | 91 | 91 | 107500000 | 350000 | 73 | ... | 88 | 93 | 77 | CM | Manchester City | Right | 483 | 2303 | 4 | 5 |
| 2 | Robert Lewandowski | 33 | 185 | 81 | Poland | 91 | 91 | 84000000 | 420000 | 75 | ... | 91 | 79 | 83 | ST | FC Barcelona | Right | 458 | 2205 | 5 | 4 |
| 0 | Lionel Messi | 35 | 169 | 67 | Argentina | 91 | 91 | 54000000 | 195000 | 76 | ... | 89 | 90 | 64 | CAM | Paris Saint-Germain | Left | 452 | 2190 | 5 | 4 |
| 4 | Kylian Mbappé | 23 | 182 | 73 | France | 91 | 95 | 190500000 | 230000 | 97 | ... | 89 | 80 | 76 | ST | Paris Saint-Germain | Right | 470 | 2177 | 4 | 4 |
| 1 | Karim Benzema | 34 | 185 | 81 | France | 91 | 91 | 64000000 | 450000 | 80 | ... | 88 | 83 | 78 | CF | Real Madrid CF | Right | 455 | 2147 | 4 | 4 |
| 5 | Mohamed Salah | 30 | 175 | 71 | Egypt | 90 | 90 | 115500000 | 270000 | 91 | ... | 89 | 82 | 75 | RW | Liverpool | Left | 471 | 2226 | 4 | 3 |
| 8 | C. Ronaldo dos Santos Aveiro | 37 | 187 | 83 | Portugal | 90 | 90 | 41000000 | 220000 | 83 | ... | 92 | 78 | 75 | ST | Manchester United | Right | 445 | 2159 | 5 | 4 |
| 9 | Virgil van Dijk | 30 | 193 | 92 | Netherlands | 90 | 90 | 98000000 | 230000 | 91 | ... | 60 | 71 | 86 | CB | Liverpool | Right | 461 | 2117 | 4 | 3 |
| 17 | Joshua Kimmich | 27 | 177 | 75 | Germany | 89 | 90 | 105500000 | 130000 | 60 | ... | 72 | 87 | 79 | CDM | FC Bayern München | Right | 473 | 2283 | 4 | 4 |
| 13 | Carlos Henrique Venancio Casimiro | 30 | 185 | 84 | Brazil | 89 | 89 | 86000000 | 240000 | 66 | ... | 73 | 75 | 90 | CDM | Manchester United | Right | 460 | 2209 | 3 | 3 |
10 rows × 23 columns
Top goalies :¶
In [24]:
goalies = fifa[fifa['Best Position'] == 'GK']
goalies[general_features].sort_values(['Overall','TotalStats','Value(in Euro)'], ascending = False).head(10)
Out[24]:
| Full Name | Age | Height(in cm) | Weight(in kg) | Nationality | Overall | Potential | Value(in Euro) | Wage(in Euro) | Sprint Speed | ... | Shooting Total | Passing Total | Physicality Total | Best Position | Club Name | Preferred Foot | BaseStats | TotalStats | International Reputation | Weak Foot Rating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | Manuel Neuer | 36 | 193 | 93 | Germany | 90 | 90 | 13500000 | 72000 | 60 | ... | 88 | 91 | 91 | GK | FC Bayern München | Right | 501 | 1535 | 5 | 4 |
| 6 | Thibaut Courtois | 30 | 199 | 96 | Belgium | 90 | 91 | 90000000 | 250000 | 52 | ... | 89 | 75 | 89 | GK | Real Madrid CF | Left | 473 | 1334 | 4 | 3 |
| 16 | Ederson Santana de Moraes | 28 | 188 | 86 | Brazil | 89 | 91 | 88000000 | 210000 | 63 | ... | 82 | 93 | 88 | GK | Manchester City | Left | 502 | 1583 | 3 | 3 |
| 18 | Alisson Ramses Becker | 29 | 191 | 91 | Brazil | 89 | 90 | 79000000 | 190000 | 49 | ... | 85 | 85 | 90 | GK | Liverpool | Right | 489 | 1437 | 3 | 3 |
| 14 | Jan Oblak | 29 | 188 | 87 | Slovenia | 89 | 91 | 85500000 | 100000 | 58 | ... | 90 | 78 | 87 | GK | Atlético de Madrid | Right | 479 | 1402 | 5 | 3 |
| 27 | Marc-André ter Stegen | 30 | 187 | 85 | Germany | 88 | 89 | 68500000 | 210000 | 50 | ... | 85 | 87 | 85 | GK | FC Barcelona | Right | 480 | 1443 | 4 | 4 |
| 26 | Keylor Navas | 35 | 185 | 80 | Costa Rica | 88 | 88 | 10000000 | 85000 | 53 | ... | 84 | 75 | 87 | GK | Paris Saint-Germain | Right | 478 | 1428 | 3 | 3 |
| 22 | Gianluigi Donnarumma | 23 | 196 | 90 | Italy | 88 | 92 | 103500000 | 110000 | 55 | ... | 83 | 79 | 85 | GK | Paris Saint-Germain | Right | 478 | 1375 | 3 | 3 |
| 37 | Mike Maignan | 26 | 191 | 89 | France | 87 | 90 | 80000000 | 90000 | 53 | ... | 82 | 85 | 85 | GK | AC Milan | Right | 477 | 1496 | 2 | 4 |
| 35 | David De Gea Quintana | 31 | 192 | 76 | Spain | 87 | 87 | 42000000 | 150000 | 50 | ... | 80 | 76 | 84 | GK | Manchester United | Right | 468 | 1415 | 4 | 3 |
10 rows × 23 columns
From which country do most players come from?¶
- Define the best players by their : Overall rating -> Total Statsistics -> Value.
In [25]:
best = fifa.sort_values(['Overall','TotalStats','Value(in Euro)'], ascending = False)
x = best.Nationality.value_counts().head(20)
colors = ['red', 'gold', 'orange', 'blue', 'turquoise', 'green', 'indigo', 'purple', 'brown', 'teal']
fig = go.Figure([go.Bar(x=x.index, y=x.values, marker_color=colors)])
fig.update_layout(width=900, height=500, title='Top 20 origin nationalities :' , xaxis_title='Nationality', yaxis_title='Number of Players')
fig.show()
Import coordinates for countries display :¶
In [26]:
coordinates = pd.read_csv("https://raw.githubusercontent.com/EylonYehiel/projects/main/Data/world_and_usa_states_coordinated.csv")
coordinates = coordinates[['country', 'latitude', 'longitude']].rename(columns={'country':'Nationality'})
coordinates.iloc[73,0] = 'England' # Adjust the several names of England int one
cofifa = fifa.merge(coordinates, on = 'Nationality')
cofifa = cofifa[['Full Name','Nationality','latitude', 'longitude','Overall', 'Value(in Euro)', 'Age', 'Club Name','International Reputation']]
cofifa.sample(7)
Out[26]:
| Full Name | Nationality | latitude | longitude | Overall | Value(in Euro) | Age | Club Name | International Reputation | |
|---|---|---|---|---|---|---|---|---|---|
| 1343 | Farès Chaïbi | France | 46.227638 | 2.213749 | 69 | 3300000 | 19 | Toulouse Football Club | 1 |
| 4217 | Jeremie Frimpong | Netherlands | 52.132633 | 5.291266 | 80 | 31500000 | 21 | Bayer 04 Leverkusen | 1 |
| 14284 | Rareș Ilie | Romania | 45.943161 | 24.966760 | 71 | 4400000 | 19 | OGC Nice | 1 |
| 12241 | Niko Hämäläinen | Finland | 61.924110 | 25.748151 | 66 | 975000 | 25 | Queens Park Rangers | 1 |
| 2363 | Francis Amuzu | Belgium | 50.503887 | 4.469936 | 72 | 3800000 | 22 | RSC Anderlecht | 1 |
| 3716 | Max Hagemoser | Germany | 51.165691 | 10.451526 | 58 | 425000 | 19 | F.C. Hansa Rostock | 1 |
| 14390 | Daniel Florea | Romania | 45.943161 | 24.966760 | 66 | 425000 | 34 | FC Voluntari | 1 |
Players origin distribution on the map:¶
In [27]:
world_map = folium.Map(location=[0, 0], zoom_start=1.5, width=850, height=500)
mc = MarkerCluster()
for idx, row in cofifa.iterrows():
if not math.isnan(row['longitude']) and not math.isnan(row['latitude']):
mc.add_child(Marker([row['latitude'], row['longitude']], popup=f"<b>Full Name:</b>{row['Full Name']}\n<b>\nClub Name:</b> ({row['Club Name']})<br><b>Nationality:</b> {row['Nationality']}<br><b>Age:</b> {row['Age']}"))
world_map.add_child(mc)
world_map
Out[27]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Nationalities frequency of Top 500 players (Overall rate):¶
- The frequency calculated by overall rating sum.
In [28]:
best2 = best.head(500).groupby('Nationality').sum().sort_values('Overall',ascending = False).reset_index()
fig = px.choropleth(best2, locations="Nationality", color="Overall", color_continuous_scale="Reds", hover_data=["Nationality", "Overall"], locationmode="country names")
fig.update_layout(title="Overall Ratings by Nationality", width=1000, height=600)
fig.update_layout(geo=dict(showframe=True, showcoastlines=False, projection_type='robinson'))
fig.show()
best2 = best2.set_index('Nationality')
best2.head(10)[['Overall']]
Out[28]:
| Overall | |
|---|---|
| Nationality | |
| Spain | 5512 |
| Brazil | 4117 |
| France | 3887 |
| Germany | 3321 |
| England | 3221 |
| Italy | 2477 |
| Portugal | 2399 |
| Argentina | 2060 |
| Netherlands | 1640 |
| Belgium | 1091 |
- Greatest scores belongs to the countries of Europe & South America.
Countries with the highest total value of All players:¶
In [29]:
best2 = best.groupby('Nationality').sum().sort_values('Overall',ascending = False)
best2 = best2.head(25).sort_values('Value(in Euro)', ascending=False)
color_scale = np.linspace(0, 1, len(best2))
colors = [f'rgb({int(c*255)}, 0, {int((1-c)*255)})' for c in color_scale]
fig = go.Figure(data=[go.Bar(x=best2.index, y=best2['Value(in Euro)'], marker=dict(color =colors))])
fig.update_layout(xaxis_title='Country', yaxis_title='Value (in Euro)', title='Sum of players\' values of Each Country:', xaxis_tickangle=-45, height = 600)
fig.show()
best2.head(10)[['Value(in Euro)']]
Out[29]:
| Value(in Euro) | |
|---|---|
| Nationality | |
| Spain | 5184394999 |
| France | 4630494998 |
| England | 4279005000 |
| Brazil | 4272774989 |
| Germany | 3482149998 |
| Argentina | 2946924997 |
| Italy | 2631509999 |
| Portugal | 2416090000 |
| Netherlands | 1936534999 |
| Belgium | 1289024999 |
In [30]:
w = best.head(500).groupby('Nationality').size().sort_values(ascending = False)
fig = px.pie(w, values=w, names=w.index, title='Top 500 Players origin distribution:')
fig.update_layout(height = 700, width = 1050)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
Data expansion:¶
- The following data set only includes international matches between national teams.
In [31]:
dat = pd.read_csv("https://raw.githubusercontent.com/EylonYehiel/projects/main/Data/goalscorers.csv")
dat.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 41008 entries, 0 to 41007 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 41008 non-null object 1 home_team 41008 non-null object 2 away_team 41008 non-null object 3 team 41008 non-null object 4 scorer 40959 non-null object 5 minute 40750 non-null float64 6 own_goal 41008 non-null bool 7 penalty 41008 non-null bool dtypes: bool(2), float64(1), object(5) memory usage: 2.0+ MB
In [32]:
goals_count = dat['team'].value_counts().reset_index()
goals_count.columns = ['Country', 'Goals']
fig = px.pie(goals_count, values='Goals', names='Country', title='Precentage (& amount) of international goals over history by Country:')
fig.update_layout(height = 800, width = 1200)
fig.update_traces(textposition='inside', textinfo='percent')
fig.show()
In [33]:
fig = px.histogram(dat,x = "minute", color="penalty", color_discrete_sequence=['purple','red'])
fig.update_layout(title='Goal minute distribution:', xaxis_title='Minute of Game', height = 600)
fig.show()
In [34]:
dat = dat[dat.own_goal == False] # Ignoring own-goals
dat['goal'] = 1 # Numerize the significance of each row (a goal)
dat['cumulative'] = dat.groupby('scorer')['goal'].cumsum()
dat.set_index('date', inplace = True)
cdat = dat.sort_values('cumulative',ascending = False).reset_index()
In [35]:
cdat['date'] = pd.to_datetime(cdat['date'])
cdat['day'] = cdat['date'].dt.day # Extract the day from the date
top = cdat.groupby('scorer').max().sort_values('cumulative', ascending=False).head(100).index
fallafel = cdat[cdat.scorer.isin(top)]
fig = px.scatter(fallafel.reset_index(), x='date', y='cumulative', color='scorer', hover_name='scorer',
hover_data=['scorer', 'date','day' ,'team', 'cumulative'])
fig.update_traces(mode='lines+markers', marker=dict(symbol='circle', size=5, opacity=0.8))
# option for adding a slider
# fig.update_xaxes(type='date', rangeslider_visible=True)
fig.update_layout(title="Top 100 international scorers of all times:", width=1050, height=600)
fig.show()
- Try to open a window inside the chart for zoom-in.
- Double-click a name in the list to view its isolated information.
Football teams Comparison:¶
In [36]:
fig = px.scatter_3d(data_frame=fifa.head(35), x='Overall', y='Wage(in Euro)', z='Value(in Euro)', color='Club Name', hover_name='Full Name')
fig.update_layout(width=800, height=600, title='Overall Rating, Value and Wage by Club - Top 35:')
fig.show()
- We can already see which teams are leading the board.
In [37]:
squad = fifa[general_features].groupby('Club Name').head(16) # Top 16 players - club's best squad
squad = squad.groupby('Club Name').mean().sort_values('Overall', ascending = False)
squad.head()
Out[37]:
| Age | Height(in cm) | Weight(in kg) | Overall | Potential | Value(in Euro) | Wage(in Euro) | Sprint Speed | Defending Total | Dribbling Total | Pace Total | Shooting Total | Passing Total | Physicality Total | BaseStats | TotalStats | International Reputation | Weak Foot Rating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Club Name | ||||||||||||||||||
| Manchester City | 27.2500 | 183.375 | 78.5000 | 85.9375 | 87.6250 | 73156250.0 | 202812.5 | 74.0625 | 69.6250 | 81.3125 | 75.1250 | 69.8125 | 78.8125 | 76.5000 | 451.1875 | 2064.0000 | 2.8750 | 3.500 |
| Real Madrid CF | 28.0000 | 181.500 | 76.7500 | 85.1875 | 87.3750 | 56437500.0 | 215625.0 | 79.6875 | 63.7500 | 80.8750 | 80.6875 | 70.1250 | 78.3750 | 74.8750 | 448.6875 | 2045.0625 | 2.6875 | 3.750 |
| Liverpool | 27.4375 | 183.375 | 77.6875 | 85.0000 | 87.0000 | 62562500.0 | 160312.5 | 75.7500 | 68.6875 | 80.3750 | 77.0625 | 67.7500 | 76.3750 | 78.1875 | 448.4375 | 2045.9375 | 2.5625 | 3.125 |
| Paris Saint-Germain | 27.6875 | 180.750 | 74.1875 | 85.0000 | 87.2500 | 59093750.0 | 119187.5 | 73.5625 | 65.5625 | 82.4375 | 78.6250 | 72.3750 | 78.5000 | 75.3125 | 452.8125 | 2032.1250 | 3.0000 | 3.625 |
| FC Bayern München | 25.8125 | 183.375 | 78.1250 | 84.5625 | 87.3125 | 58187500.0 | 84625.0 | 78.6875 | 64.2500 | 81.5000 | 80.6875 | 73.0625 | 77.4375 | 75.4375 | 452.3750 | 2061.0625 | 3.1250 | 3.500 |
In [38]:
pxq = px.colors.qualitative
club_names = squad.index
colors = pxq.Pastel + pxq.Vivid + pxq.G10 + pxq.Vivid + pxq.Bold + pxq.T10 + pxq.Alphabet
# Define function to rank clubs based on a specific attribute and create a bar chart trace to visualize the rankings
def rank_clubs_by_attribute_mean(attribute, title=None):
club_colors = dict(zip(club_names, colors))
grouped = squad.reset_index()
grouped = grouped.sort_values(by=attribute, ascending=False)[:10][::-1]
ncolors = [club_colors.get(club_name, 'gray') for club_name in grouped['Club Name']]
trace = go.Bar(x=grouped[attribute], y=grouped['Club Name'], orientation='h', marker_color=ncolors)
return trace
In [39]:
attributes = ['Value(in Euro)', 'Wage(in Euro)', 'Overall', 'Dribbling Total', 'Defending Total', 'Physicality Total', 'Pace Total', 'Sprint Speed', 'Shooting Total', 'Passing Total', 'Weak Foot Rating', 'International Reputation']
positions = [(1,1), (1,2), (2,1), (2,2), (3,1), (3,2), (4,1), (4,2), (5,1), (5,2), (6,1), (6,2)]
fig = make_subplots(rows=6, cols=2, subplot_titles = attributes, horizontal_spacing = 0.2, vertical_spacing=0.05)
def uwt(att,row,col):
fig.add_trace(go.Bar(rank_clubs_by_attribute_mean(att, title = att), name=att), row, col)
for att, pos in zip(attributes, positions):
uwt(att, *pos)
fig.update_layout(height=1800, width=1150 ,title_text="Ranking of clubs by various attributes' means:", showlegend=False)
fig.show()
Summing all attributes to assess the total teams' abilities:¶
In [40]:
squad = fifa[general_features].groupby('Club Name').head(16) # Top 16 players - club's best squad
squad = squad.groupby('Club Name').mean().sort_values('Overall', ascending = False)
scores = []
for i in range(len(squad)):
scores.append(sum(squad.iloc[i][3:5]) + sum(squad.iloc[i][7:]))
squad['Score'] = scores
nsquad = squad[['Score']].sort_values('Score', ascending = False).head(25)
nsquad
Out[40]:
| Score | |
|---|---|
| Club Name | |
| FC Bayern München | 3223.0000 |
| Manchester City | 3220.3750 |
| Real Madrid CF | 3201.1250 |
| Liverpool | 3196.2500 |
| Paris Saint-Germain | 3190.1875 |
| Atlético de Madrid | 3169.4375 |
| FC Barcelona | 3157.2500 |
| Chelsea | 3145.5000 |
| Juventus | 3141.6250 |
| Tottenham Hotspur | 3127.3125 |
| AC Milan | 3122.2500 |
| Borussia Dortmund | 3118.5000 |
| Inter | 3093.7500 |
| Sevilla FC | 3088.3750 |
| Manchester United | 3086.5625 |
| Aston Villa | 3075.8125 |
| Athletic Club de Bilbao | 3072.3750 |
| RB Leipzig | 3062.5000 |
| River Plate | 3055.5625 |
| Roma | 3055.3750 |
| Ajax | 3054.8125 |
| Villarreal CF | 3049.8750 |
| Napoli | 3040.8750 |
| Leicester City | 3030.1250 |
| Bayer 04 Leverkusen | 3029.0000 |
In [41]:
fig = px.bar(nsquad[::-1], x='Score', y=nsquad.index[::-1], title='Teams best stats - top 25:',
orientation='h', color='Score', color_continuous_scale='Viridis')
fig.update_layout(height=600, xaxis_title='Score', yaxis_title='Team')
fig.show()
Thanks for Watching !
